Energy-efficient resource allocation for UAV-aided full-duplex OFDMA wireless powered IoT communication networks

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-11-01 DOI:10.1016/j.jksuci.2024.102225
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Abstract

The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.
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无人机辅助全双工 OFDMA 无线供电物联网通信网络的高能效资源分配
在多种无人飞行器(UAV)和全双工技术的支持下,无线供电的物联网(IoT)网络发展迅速,为同时进行数据传输和能量采集开辟了新的途径。在这种情况下,优化能源效率(EE)对于确保网络的可持续高效运行至关重要。本文提出了一种在多无人机辅助的无线供电物联网网络中优化能效的新方法,重点是在正交频分多址(OFDMA)框架内平衡上行数据传输速率和系统总能耗。这涉及将 EE 优化问题表述为多目标优化问题(MOOP),包括上行链路总速率最大化和系统总能耗最小化,然后使用 Tchebycheff 方法将其转化为单目标优化问题(SOOP)。为了解决以组合变量和耦合约束为特征的 SOOP 的非凸性质,我们开发了一种结合了块坐标下降 (BCD) 和连续凸逼近 (SCA) 的迭代算法。该算法将子载波分配和功率控制子问题分离开来,加入惩罚项以放松整数约束,并交替解决每个子问题,直至达到收敛。仿真结果表明,我们提出的方法在关键性能指标上优于基准方法,突出了我们的框架在提高现实世界无人机辅助无线网络的效率和可持续性方面的实际适用性和稳健性。我们的研究结果为未来研究提供了启示,有助于将所提出的框架扩展到涉及无人机动态移动性、多跳通信和增强能源管理的场景,从而支持下一代可持续通信系统的开发。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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